# Prompt Injection Taxonomy  

** NEW 1.5 version built into interactive Githug Pages **

[For the new live site click here](https://arcanum-sec.github.io/arc_pi_taxonomy)

🚧 **This repository is in active development. Contributions and feedback are welcome!** 🚧  

This repository provides a structured taxonomy of **prompt injection attacks**, categorizing different types of attack intents, techniques, and evasions. It serves as a **resource for security researchers, AI developers, and red teamers** working to understand and mitigate the risks associated with prompt injection in AI-driven applications.  

## Structure  

The taxonomy is organized into the following key areas:  

### **Attack Intents** [`/attack_intents/`]  
   - Describes the **goals and objectives** that attackers aim to achieve through prompt injection.  

### **Attack Techniques** [`/attack_techniques/`]  
   - Details various **methods and approaches** used to execute prompt injection attacks.  

### **Attack Evasions** [`/attack_evasions/`]  
   - Covers **methods used to hide, obfuscate, or avoid detection** of prompt injection attacks.  

### **Additional Resources**  

- **LLM DevOps Infrastructure Security Assessment Table** [`/ecosystem/README.md`]  
   - A structured assessment framework for evaluating the **security of AI-enabled infrastructures**.  

- **Example Probes for AI-Enabled Forms & Endpoints** [`/probes.md`]  
   - A **curated list of example probes** to help identify AI-enabled web forms and endpoints in applications.  

## Usage  

Each folder contains individual Markdown files with **detailed descriptions of specific components** within each category. The taxonomy is designed to be clear, structured, and easy to navigate.  

## Who Is This For?  

**Security researchers** analyzing LLM vulnerabilities  
**Red teams** assessing AI-driven applications  
**Developers** securing AI-powered systems  
**Academics & enthusiasts** studying adversarial AI  

## Contributing  

This project is actively evolving! Contributions, corrections, and additions are encouraged. Please follow the repository’s contribution guidelines to submit new findings or improvements.  

## License & Attribution

This methodology and repository content are licensed under a [Creative Commons Attribution 4.0 International License (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/).

If you use, reference, or build upon this methodology, you **must** explicitly attribute the original work as follows:

> *"This methodology/content is based on the [Arcanum Prompt Injection Taxonomy](https://github.com/Arcanum-Sec/arc_pi_taxonomy/) by Jason Haddix ([Arcanum Information Security](https://arcanum-sec.com/))."*

Full license details are available in the [LICENSE.md](LICENSE.md) file.